Asssumes you have aready run: - config - workflow_deepseaNiN_Start

You now have the objects needed to explore the dataset and find the best way to apply biogeographic splits. This is a pre-step before running the workflow again on the split dataset to explore possible LKMs.

Finding Regional (biogeographic) Splits

This requires exploring the dataset to theorise the best ways to split the data with: - environmental biplots/colourings

then applying the splits.

Libraries

library(plotly)
library(readxl)

Exploring biplots

make full dataset to explore

should include all env Vars and otus to explore, and make species richness variable

#add species data and sp richness variable
env_sub_meta1<-cbind(env,otu_6)
env_sub_meta1$spRich<-rowSums(otu_6[,-c(1:which(colnames(otu_6)=="Zoanthidae"))]!=0)

#rename X
env_sub_meta1$X <- env$X.y
env_sub_meta1 <-env_sub_meta1 %>% select (-c(X.y))

#add samplID
env_sub_meta1$SampID<-envSel$SampID

Add provisional biotope data to env file

Note that biotopes were last assigned in march 2022 and therefore there are some addional samples that have not yet got a biotope assigned. These should just be NAs

biotopeInfo<-read_xlsx(file.path(dataPath, "inputs/MAREANO_provisional_biotope_classification_0322.xlsx"), sheet=1) %>%
  select(-c(x_coordinate_UTM33N, y_coordinate_UTM33N))

env_sub_meta<-left_join(env_sub_meta1,biotopeInfo)
Joining, by = "SampID"

a colour palette for discrete plots (see later in script)

cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

Find min max of vars for scales

colnames(env_sub_meta)
  [1] "X"                                     "bathy"                                
  [3] "diffME3"                               "diffME9"                              
  [5] "landscape"                             "msr1_mag"                             
  [7] "msr5_mag"                              "salt_max"                             
  [9] "salt_mean"                             "salt_min"                             
 [11] "salt_std"                              "slope3"                               
 [13] "slope9"                                "spd_max"                              
 [15] "spd_mean"                              "spd_min"                              
 [17] "spd_std"                               "temp_max"                             
 [19] "temp_mean"                             "temp_min"                             
 [21] "temp_std"                              "u_bott_mean"                          
 [23] "v_bott_mean"                           "X.y"                                  
 [25] "Y"                                     "MLDmax_Robinson"                      
 [27] "MLDmean_Robinson"                      "MLDmin_Robinson"                      
 [29] "MLDsd_Robinson"                        "Smax_Robinson"                        
 [31] "Smean_Robinson"                        "Smin_Robinson"                        
 [33] "Ssd_Robinson"                          "Tmax_Robinson"                        
 [35] "Tmean_Robinson"                        "Tmin_Robinson"                        
 [37] "Tsd_Robinson"                          "CDirmax_Robinson"                     
 [39] "CDirmean_Robinson"                     "CDirmin_Robinson"                     
 [41] "CDirsd_Robinson"                       "CSpdmax_Robinson"                     
 [43] "CSpdmean_Robinson"                     "CSpdmin_Robinson"                     
 [45] "CSpdsd_Robinson"                       "Umax_Robinson"                        
 [47] "Umean_Robinson"                        "Umin_Robinson"                        
 [49] "Usd_Robinson"                          "Vmax_Robinson"                        
 [51] "Vmean_Robinson"                        "Vmin_Robinson"                        
 [53] "Vsd_Robinson"                          "BO22_carbonphytoltmax_bdmean"         
 [55] "BO22_carbonphytoltmax_ss"              "BO22_carbonphytoltmin_bdmean"         
 [57] "BO22_carbonphytoltmin_ss"              "BO22_carbonphytomean_bdmean"          
 [59] "BO22_carbonphytomean_ss"               "BO22_carbonphytorange_bdmean"         
 [61] "BO22_carbonphytorange_ss"              "BO22_chloltmax_bdmean"                
 [63] "BO22_chloltmax_ss"                     "BO22_chloltmin_bdmean"                
 [65] "BO22_chloltmin_ss"                     "BO22_chlomean_bdmean"                 
 [67] "BO22_chlomean_ss"                      "BO22_chlorange_bdmean"                
 [69] "BO22_chlorange_ss"                     "BO22_dissoxltmax_bdmean"              
 [71] "BO22_dissoxltmin_bdmean"               "BO22_dissoxmean_bdmean"               
 [73] "BO22_dissoxrange_bdmean"               "BO22_icecoverltmax_ss"                
 [75] "BO22_icecoverltmin_ss"                 "BO22_icecovermean_ss"                 
 [77] "BO22_icecoverrange_ss"                 "BO22_icethickltmax_ss"                
 [79] "BO22_icethickltmin_ss"                 "BO22_icethickmean_ss"                 
 [81] "BO22_icethickrange_ss"                 "BO22_ironltmax_bdmean"                
 [83] "BO22_ironltmin_bdmean"                 "BO22_ironmean_bdmean"                 
 [85] "BO22_ironrange_bdmean"                 "BO22_nitrateltmax_bdmean"             
 [87] "BO22_nitrateltmin_bdmean"              "BO22_nitratemean_bdmean"              
 [89] "BO22_nitraterange_bdmean"              "BO22_phosphateltmax_bdmean"           
 [91] "BO22_phosphateltmin_bdmean"            "BO22_phosphatemean_bdmean"            
 [93] "BO22_phosphaterange_bdmean"            "BO22_ppltmax_bdmean"                  
 [95] "BO22_ppltmax_ss"                       "BO22_ppltmin_bdmean"                  
 [97] "BO22_ppltmin_ss"                       "BO22_ppmean_bdmean"                   
 [99] "BO22_ppmean_ss"                        "BO22_pprange_bdmean"                  
[101] "BO22_pprange_ss"                       "BO22_silicateltmax_bdmean"            
[103] "BO22_silicateltmin_bdmean"             "BO22_silicatemean_bdmean"             
[105] "BO22_silicaterange_bdmean"             "MS_biogeo05_dist_shore_5m"            
[107] "gmorph"                                "sedclass"                             
[109] "cobB"                                  "gravel"                               
[111] "mud"                                   "rock"                                 
[113] "sand"                                  "coords.x1"                            
[115] "coords.x2"                             "optional"                             
[117] "swDensRob_avs"                         "MLDmean_bathy"                        
[119] "MLDmin_bathy"                          "MLDmax_bathy"                         
[121] "Actiniaria.red"                        "Actiniaria.violet"                    
[123] "Actiniaria.white"                      "Actiniaria.yellow"                    
[125] "Actiniaria_buried.redish"              "Actiniaria_buried.yellow"             
[127] "Actiniaria_buried_dark"                "Actiniaria_epizoic"                   
[129] "Actiniaria_yellow_stolon"              "Actinostola_callosa"                  
[131] "Alcyonidium_sp."                       "Alcyonium_digitatum"                  
[133] "Amphicteis_ninonae"                    "Antedonoidea"                         
[135] "Antho_dichotoma"                       "Anthomastus_sp."                      
[137] "Anthothela_grandiflora"                "Aphroditidae"                         
[139] "Aporrhais_sp."                         "Arctica_islandica"                    
[141] "Asbestopluma_furcata"                  "Asbestopluma_pennatula"               
[143] "Ascidia_sp."                           "Ascidia_sp..transparent"              
[145] "Ascidia_sp._veined"                    "Ascidia_sp._violet"                   
[147] "Ascidia_virginea"                      "Ascidiacea_colonial_encrusting"       
[149] "Ascidiacea_colonial_encrusting.orange" "Ascidiacea_colonial_encrusting.white" 
[151] "Ascidiacea_colonial_erect"             "Ascidiacea_solitary"                  
[153] "Ascidiacea_solitary_big"               "Asconema_setubalense"                 
[155] "Astacidea"                             "Asterias_rubens"                      
[157] "Asteronyx_loveni"                      "Asterozoa"                            
[159] "Astropecten_irregularis"               "Astropectinidae"                      
[161] "Axinella_infundibuliformis"            "Axinellidae"                          
[163] "Bacterial_mat"                         "Balanus_balanus"                      
[165] "Balticina_sp."                         "Bathybiaster_vexillifer"              
[167] "Bathycrinus_carpenterii"               "Bathyplotes_natans"                   
[169] "Beringius_sp."                         "Bolocera_tuediae"                     
[171] "Bonelliidae"                           "Botryllus_sp."                        
[173] "Bourgueticrinina"                      "Brada_sp."                            
[175] "Bryozoa_calcareous_branched"           "Bryozoa_coral"                        
[177] "Bryozoa_encrusting"                    "Bryozoa_soft_branched"                
[179] "Bryozoa_soft_bush"                     "Buccinidae"                           
[181] "Buccinum_hydrophanum"                  "Buccinum_sp."                         
[183] "Buccinum_undatum"                      "Bugula_sp."                           
[185] "Cancer_pagurus"                        "Candelabrum_sp."                      
[187] "Caulophacus_arcticus"                  "Celleporidae"                         
[189] "Ceramaster.Hippasterias"               "Ceramaster_granularis"                
[191] "Cerianthidae"                          "Cerianthidae.cup_coral"               
[193] "Cerianthidae.dark"                     "Cerianthidae.violet"                  
[195] "Cerianthus_lloydii"                    "Cerianthus_vogti"                     
[197] "Chaetopterus_sp."                      "Chelonaplysilla_sp."                  
[199] "Chionoecetes_opilio"                   "Chlamys_sp."                          
[201] "Chondrocladia_gigantea"                "Cidaris_cidaris"                      
[203] "Ciona_intestinalis"                    "Cirripedia"                           
[205] "Cladorhiza_gelida"                     "Cladorhiza_sp."                       
[207] "Cladorhizidae"                         "Cladorhizidae_bottlebrush"            
[209] "Cladorhizidae_branched"                "Cladorhizidae_bush"                   
[211] "Cladorhizidae_stalked"                 "Clavularia_borealis"                  
[213] "Clavulariidae"                         "Colossendeis_angusta"                 
[215] "Colossendeis_proboscidea"              "Colossendeis_sp."                     
[217] "Colus_sp."                             "Conocrinus_lofotensis"                
[219] "Corella_parallelogramma"               "Corymorpha_glacialis"                 
[221] "Corymorpha_nutans"                     "Corymorpha_sand_stolon"               
[223] "Corymorpha_sp."                        "Crangonidae"                          
[225] "Craniella_cranium"                     "Craniella_sp."                        
[227] "Craniella_zetlandica"                  "Crisia_sp."                           
[229] "Crossaster_papposus"                   "Crossaster_sp."                       
[231] "Crossaster_squamatus"                  "Ctenodiscus_crispatus"                
[233] "Ctenophora_benthic"                    "Cucumaria_frondosa"                   
[235] "Cup_coral"                             "Dallina_septigera"                    
[237] "Dendrobeania_sp."                      "Dendrodoa_aggregata"                  
[239] "Dendronotus_sp."                       "Didemnidae"                           
[241] "Diplopteraster_multipes"               "Ditrupa_arietina"                     
[243] "Drifa_glomerata"                       "Duva_florida"                         
[245] "Dysidea_fragilis"                      "Echinoidea_irregular"                 
[247] "Echinoidea_regular"                    "Echinus_esculentus"                   
[249] "Echinus_sp."                           "Ectopleura_larynx"                    
[251] "Edwardsiidae"                          "Elpidia_glacialis"                    
[253] "Enteropneusta"                         "Eucratea_loricata"                    
[255] "Filograna_implexa"                     "Flustridae"                           
[257] "Flustrina"                             "Foraminifera_calcareous"              
[259] "Funiculina_quadrangularis"             "Geodia.Stelleta"                      
[261] "Geodia.Stryphnus"                      "Geodia_atlantica"                     
[263] "Geodia_barretti"                       "Geodia_macandrewii"                   
[265] "Geodia_phlegraei"                      "Geodia_sp."                           
[267] "Gersemia_rubiformis"                   "Geryon_trispinosus"                   
[269] "Gorgonacea"                            "Gorgonocephalus_sp."                  
[271] "Gracilechinus_acutus"                  "Gracilechinus_sp."                    
[273] "Grantia_compressa"                     "Halcampa_arctica"                     
[275] "Halcampa_sp."                          "Halcampoides_sp."                     
[277] "Halecium_sp."                          "Halichondria_sp."                     
[279] "Haliclona_sp."                         "Hamacantha_bowerbanki"                
[281] "Heliometra_glacialis"                  "Henricia_sp."                         
[283] "Henricia_sp..blue"                     "Henricia_sp..orange"                  
[285] "Henricia_sp..red"                      "Henricia_sp..violet"                  
[287] "Henricia_sp..white"                    "Henricia_sp..yellow"                  
[289] "Hexactinellida"                        "Hexactinellida_fan_shaped"            
[291] "Hexactinellida_parabol"                "Hexactinellida_urn.shaped"            
[293] "Hexadella_dedritifera"                 "Hippasteria_phrygiana"                
[295] "Hirudinea"                             "Hormathia_digitata"                   
[297] "Hormathia_nodosa"                      "Hormathia_sp."                        
[299] "Hormathiidae"                          "Horneridae"                           
[301] "Hyalonema_sp."                         "Hyas_coarctatus"                      
[303] "Hyas_sp."                              "Hydroides_norvegica"                  
[305] "Hydrozoa_feather"                      "Hydrozoa_solitary"                    
[307] "Hydrozoa_tree"                         "Hymedesmia_paupertas"                 
[309] "Hymenaster_pellucidus"                 "Hymenodiscus_coronata"                
[311] "Icasterias_panopla"                    "Isidella_lofotensis"                  
[313] "Isodictya_palmata"                     "Jasmineira_sp."                       
[315] "Kinetoskias_smitti"                    "Kolga_hyalina"                        
[317] "Kophobelemnon_stelliferum"             "Kukenthalia_borealis"                 
[319] "Lafoea_sp."                            "Laminaria_sp."                        
[321] "Laminariales"                          "Lanice_conchilega"                    
[323] "Latrunculia_sp."                       "Leieschara_sp."                       
[325] "Leptasterias_muelleri"                 "Leptasterias_sp."                     
[327] "Leptychaster_arcticus"                 "Lichenoporidae"                       
[329] "Liponema_multicorne"                   "Lithodes_maja"                        
[331] "Lithodidae"                            "Lophaster_furcifer"                   
[333] "Lophelia_pertusa"                      "Lucernaria_bathyphila"                
[335] "Luidia_ciliaris"                       "Luidia_sp."                           
[337] "Madrepora_oculata"                     "Mellonympha_mortenseni"               
[339] "Mesothuria_intestinalis"               "Molpadia_sp."                         
[341] "Molva_sp."                             "Munida_sarsi"                         
[343] "Munida_sp."                            "Munidopsis_serricornis"               
[345] "Muriceides_kuekenthali"                "Mycale_lingua"                        
[347] "Myxicola_sp."                          "Myxilla_incrustans"                   
[349] "Nemertea"                              "Nemertesia_antennina"                 
[351] "Neohela_sp."                           "Nephrops_norvegicus"                  
[353] "Nephtheidae"                           "Neptunea_despecta"                    
[355] "Neptunea_sp."                          "Nereididae"                           
[357] "Nothria_sp."                           "Oceanapia_robusta"                    
[359] "Ophiacanthidae"                        "Ophiocten_gracilis"                   
[361] "Ophiocten_sericeum"                    "Ophiocten_sp."                        
[363] "Ophiopholis_aculeata"                  "Ophiopleura_borealis"                 
[365] "Ophioscolex_glacialis"                 "Opisthobranchia"                      
[367] "Pachycerianthus_multiplicatus"         "Paguridae"                            
[369] "Paragorgia_arborea"                    "Paralithodes_camtschaticus"           
[371] "Paramuricea_placomus"                  "Parasmittina_jeffreysi"               
[373] "Parastichopus_tremulus"                "Patellogastropoda"                    
[375] "Pectinariidae"                         "Pectinidae"                           
[377] "Peltaster_placenta"                    "Pennatula_phosphorea"                 
[379] "Pennatulacea"                          "Phakellia.Axinella"                   
[381] "Phakellia_sp."                         "Phyllodoce_rosea"                     
[383] "Phyllodocidae"                         "Plicatellopsis_bowerbanki"            
[385] "Polycarpa_sp."                         "Polychaeta_fishingnet"                
[387] "Polychaeta_question_mark"              "Polychaeta_sediment_tube"             
[389] "Polychaeta_soft_thin_tube"             "Polychaeta_tube"                      
[391] "Polymastia_grimaldii"                  "Polymastia_sp."                       
[393] "Polymastiidae"                         "Polynoidae"                           
[395] "Pontaster_tenuispinus"                 "Porania.Poraniomorpha"                
[397] "Porania_sp."                           "Poraniidae"                           
[399] "Poraniomorpha_sp."                     "Poraniomorpha_tumida"                 
[401] "Porella_compressa"                     "Porella_sp."                          
[403] "Porifera_bat"                          "Porifera_big"                         
[405] "Porifera_branched"                     "Porifera_brown_papillae"              
[407] "Porifera_cupcake"                      "Porifera_dirty_yellow"                
[409] "Porifera_egg"                          "Porifera_encrusting"                  
[411] "Porifera_encrusting.bluegrey"          "Porifera_encrusting.brown"            
[413] "Porifera_encrusting.green"             "Porifera_encrusting.grey"             
[415] "Porifera_encrusting.orange"            "Porifera_encrusting.purple"           
[417] "Porifera_encrusting.red"               "Porifera_encrusting.white"            
[419] "Porifera_encrusting.yellow"            "Porifera_erect"                       
[421] "Porifera_fan"                          "Porifera_fan.big"                     
[423] "Porifera_fan.small"                    "Porifera_fan.white"                   
[425] "Porifera_lily"                         "Porifera_lollipop"                    
[427] "Porifera_medium.white"                 "Porifera_medium.yellow"               
[429] "Porifera_medium_round"                 "Porifera_parabol"                     
[431] "Porifera_small.green"                  "Porifera_small.irregular"             
[433] "Porifera_small.orange"                 "Porifera_small.round_yellow"          
[435] "Porifera_small.spikey"                 "Porifera_small.stalked"               
[437] "Porifera_small.yellow"                 "Porifera_string"                      
[439] "Porifera_urn"                          "Porifera_white_bush"                  
[441] "Porifera_window"                       "Pourtalesia_jeffreysi"                
[443] "Primnoa_resedaeformis"                 "Protanthea_simplex"                   
[445] "Pseudamussium_peslutrae"               "Pseudarchaster_parelii"               
[447] "Psolus_phantapus"                      "Psolus_sp."                           
[449] "Psolus_squamatus"                      "Pteraster_militaris"                  
[451] "Pteraster_obscurus"                    "Pteraster_pulvillus"                  
[453] "Pteraster_sp."                         "Ptychogastria_polaris"                
[455] "Quasillina_brevis"                     "Quasillina_sp."                       
[457] "Radicipes_sp."                         "Reteporella_beaniana"                 
[459] "Reteporella_sp."                       "Sabellidae"                           
[461] "Saduria_sp."                           "Scaphopoda"                           
[463] "Sclerocrangon_ferox"                   "Serpulidae"                           
[465] "Sertulariidae"                         "Siboglinidae"                         
[467] "Smittinidae"                           "Solaster_endeca"                      
[469] "Solaster_sp."                          "Solasteridae"                         
[471] "Spatangoida"                           "Spatangus_purpureus"                  
[473] "Spiochaetopterus_tubes"                "Spionidae"                            
[475] "Spirobranchus_triqueter"               "Spirontocaris_sp."                    
[477] "Stauromedusae"                         "Steletta_grubei"                      
[479] "Stelletta_sp."                         "Stichastrella_rosea"                  
[481] "Strongylocentrotus_sp."                "Stryphnus_ponderosus"                 
[483] "Styela_sp."                            "Stylasteridae"                        
[485] "Stylocordyla_borealis"                 "Swiftia_sp."                          
[487] "Sycon_sp."                             "Sycon_stalked"                        
[489] "Tentorium_semisuberites"               "Terebellida"                          
[491] "Tetilla_sp."                           "Thenea_abyssorum"                     
[493] "Thenea_levis"                          "Thenea_sp."                           
[495] "Thuiaria_obsoleta"                     "Thuiaria_thuja"                       
[497] "Tremaster_mirabilis"                   "Tubularia_indivisa"                   
[499] "Tubularia_sp."                         "Tubulariidae"                         
[501] "Tunicata_trunk"                        "Umbellula_encrinus"                   
[503] "Urasterias_lincki"                     "Urticina_sp."                         
[505] "Virgularia_mirabilis"                  "Weberella_bursa"                      
[507] "Zoanthidae"                            "gnmds1"                               
[509] "gnmds2"                                "dca1"                                 
[511] "dca2"                                 

Plots with CONTINUOUS colour scales (variables)

Bathy v Temp w gnmds ax 1
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by gnmds r6 ax1")

tb_ax1

Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_nmds1.png"),
       device = "png",
       dpi=300 )
Saving 7 x 7 in image
Bathy v Density w gnmds ax 1
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = swDensRob_avs,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Density vs Bathy - coloured by gnmds1")

tb_ax1

Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_bathy_v_dens_gnmds1.png"),
       device = "png",
       dpi=300 )
Saving 7 x 7 in image
Bathy v Temp w longitude
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = X.y),
             size = 1) +
  scale_colour_gradientn(limits = c(-107939, 1162261),
                        colors=c('red','yellow','blue'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by longitude")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_longitude.png"),
       device = "png",
       dpi=300 )
Bathy v Temp w latitude
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = Y),
             size = 1) +
  scale_colour_gradientn(limits = c(6944134, 8949734),
                        colors=c('red','yellow','blue'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by latitude")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_latitude.png"),
       device = "png",
       dpi=300 )
Bathy v Temp w iceCovLTMax
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = BO22_icecoverltmax_ss),
             size = 1) +
  scale_colour_gradientn(limits = c(0,0.93),
                        colors=c('grey','turquoise','blue'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by ice cover LT max")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_iceCovLTmax.png"),
       device = "png",
       dpi=300 )
Bathy v Salinity coloured by temp
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = Tmean_Robinson),
             size = 1) +
  scale_colour_gradientn(limits = c(-1.1, 8.5),
                        colors=c('red','yellow','green'))+
  ggtitle("Salinity (max Robinson) vs Bathy - coloured by av Temp (R)")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_salMax_temp.png"),
       device = "png",
       dpi=300 )
Salinity v Temp gnmds1
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = Tmean_Robinson)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) vs Salinity (max R) - coloured by gnmds r6 ax 1 - grey 2.5-5")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mtempRmean_v_salinityRmax_nmds1.png"),
       device = "png",
       dpi=300 )
Salinity v Temp dissoxmean
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = Tmean_Robinson)) +
  theme_classic() +
  geom_point(aes(colour = BO22_dissoxmean_bdmean),
             size = 1) +
scale_colour_gradientn(limits = c(282.5, 372.2),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) vs Salinity (max R) - coloured by dissolved oxygen")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mtempRmean_v_salinityRmax_disooxmean.png"),
       device = "png",
       dpi=300 )
TS - icecoverLTMax
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = Tmean_Robinson)) +
  theme_classic() +
  geom_point(aes(colour = BO22_icecoverltmax_ss),
             size = 1) +
  scale_colour_gradientn(limits = c(0,0.93),
                        colors=c('blue','green','red'))+
  ggtitle("Temp (AvR) v Salinity (maxR) - coloured by ice cover LT max")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mtempRmean_v_salinityRmax_iceCoveLTmax.png"),
       device = "png",
       dpi=800 )
X v Y - gnmds1
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Geography (X v Y) - coloured by gnmds1")

ggplotly(tb_ax1)

NA
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_gnmds1.png"),
       device = "png",
       dpi=300 )
Saving 7 x 7 in image
X v Y - density
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = swDensRob_avs),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$swDensRob_avs), 
                                    max(env_sub_meta$swDensRob_avs)),
                        colors=c('blue','green','red'))+
  ggtitle("Geography (X v Y) - coloured by water density")

tb_ax1

Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_swDenRobavs.png"),
       device = "png",
       dpi=800 )
Saving 7 x 7 in image
X v Y - dissox
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = BO22_dissoxmean_bdmean),
             size = 1) +
  scale_colour_gradientn(limits = c(282,373),
                        colors=c('blue','green','red'))+
  ggtitle("Geography (X v Y) - coloured by dissovled oxygen")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_dissox.png"),
       device = "png",
       dpi=800 )
X v Y - icecoverLTMax
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = BO22_icecoverltmax_ss),
             size = 1) +
  scale_colour_gradientn(limits = c(0.00001,0.93),
                        colors=c('blue','green','red'))+
  ggtitle("Geography (X v Y) - coloured by ice cover LT max - grey <0.00001")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_iceCoveLTmax.png"),
       device = "png",
       dpi=800 )
Temp v disoxltmin gnmds
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = BO22_dissoxmean_bdmean)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(-2.3, 2),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) v disox mean - coloured by gnmds ax 1")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_TmeanR_v_dissoxltmin_gnmds1.png"),
       device = "png",
       dpi=300 )
Temp v disoxltmin bathy
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = BO22_dissoxmean_bdmean)) +
  theme_classic() +
  geom_point(aes(colour = bathy),
             size = 1) +
  scale_colour_gradientn(limits = c(-702, -38),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) v disox mean - coloured by bathymetry")

tb_ax1
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mTmeanR_v_dissoxltmin_bathy.png"),
       device = "png",
       dpi=300 )

Plots with DISCRETE colour scales (variables)

X v Y with mld-bathy categories

dis_split <- ggplot(data = env,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = MLDmean_bathy),
             size = 1) +
  scale_colour_manual(values=cbPalette)+
 # scale_colour_brewer(palette = "Set3") +
  ggtitle("Easting vs Northing - coloured by Mixed layer depth proximity")

dis_split
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_MLDmeanBathy.png"),
       device = "png",
       dpi=300 )

categorise gnmds

env_sub_meta$ax1cat<-cut(env_sub_meta$gnmds1, 
      breaks=c(-3.2,-3,-2,-1,0,1,2,3,3.46))

env_sub_meta$ax2cat<-cut(env_sub_meta$gnmds2, 
      breaks=c(-1.9,-1,0,1,2,3,4,4.9))

X v Y with gnmds ax 1 as HC categories

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = ax1cat),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Spectral") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by gnmds axis 1 HC units")

dis_split
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_ax1cat.png"),
       device = "png",
       dpi=300 )

categorise temp 5.1

env_sub_meta$temp5_1<-cut(env_sub_meta$Tmean_Robinson, 
      breaks=c(-1.1, 5.1, 8.5))
#

X v Y with temp 5.1

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = temp5_1),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by temp thresholded at 5.1*C")

dis_split
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_temp5_1.png"),
       device = "png",
       dpi=300 )

categorise dissox

env_sub_meta$dissoxav305<-cut(env_sub_meta$BO22_dissoxmean_bdmean, 
     # breaks=c(256, 282, 360)) #ltmin
     breaks=c(282.5,305,372.2),
     labels=c("lowO2","hiO2")) #mean
#

X v Y with dissox

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = dissoxav305),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by dissox av thresholded at 305")

dis_split
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_dissoxav305.png"),
       device = "png",
       dpi=300 )

categorise iceCovLTMax

env_sub_meta$iceMx_gt0<-cut(env_sub_meta$BO22_icecoverltmax_ss, 
      breaks=c(0, 0.00001, 0.92838))
#

X v Y with temp 5.1

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = iceMx_gt0),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by ice LT Max thresholded at >0")

dis_split
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_iceLTmx_gt0.png"),
       device = "png",
       dpi=300 )

X v Y with gnmds ax 2 as HC categories

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = ax2cat),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Spectral") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by gnmds axis 2 HC units")

dis_split
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_ax2cat.png"),
       device = "png",
       dpi=300 )

Gnmds w temp 5.1


t_gmo <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by temp 5.1 threshold",
          subtitle = "First run") +
  geom_point(aes(colour = factor(temp5_5))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

t_gmo
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_temp5_1.png"),
       device = "png",
       dpi=300 )

Gnmds w dissox 305


o_gmo <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by dissox 305 threshold",
          subtitle = "First run") +
  geom_point(aes(colour = factor(dissox305))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

o_gmo
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_dissox305.png"),
       device = "png",
       dpi=300 )

Gnmds w iceLT max >0


i_gmo <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by ice max >0 threshold",
          subtitle = "First run") +
  geom_point(aes(colour = factor(iceMx_gt0))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

i_gmo
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_iceMxgt0.png"),
       device = "png",
       dpi=300 )
comp<-i_gmo+o_gmo+t_gmo
Save the plot


##### Save some outputs

ggexport(comp,
          filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_compare.png"),
          width = 1500,
          height = 500)

X v Y with landscape

dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = as.factor(landscape)),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by landscape")

dis_split

Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_landscape.png"),
       device = "png",
       dpi=300 )
Saving 7 x 7 in image

GNMDS PLOTS

colour palette to cope with up to 25 categorical colours
c25 <- c(
  "dodgerblue2", "#E31A1C", # red
  "green4",
  "#6A3D9A", # purple
  "#FF7F00", # orange
  "black", "gold1",
  "skyblue2", "#FB9A99", # lt pink
  "palegreen2",
  "#CAB2D6", # lt purple
  "#FDBF6F", # lt orange
  "gray70", "khaki2",
  "maroon", "orchid1", "hiDens_b1500pink1", "blue1", "steelblue4",
  "darkturquoise", "green1", "yellow4", "yellow3",
  "darkorange4", "brown"
)

gnmds with density categories

summary(env_sub_meta$swDensRob_avs)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1035    1036    1037    1037    1038    1041 
env_sub_meta$densCat<-cut(env_sub_meta$swDensRob_avs, 
      breaks=c(1035,1036,1037,1038,1039,1040,1041))

p_dens <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by density bins") +
  geom_point(aes(colour = factor(densCat))) +
   scale_colour_manual(values=c25)+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_dens)

NA
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_densityCat.png"),
       device = "png",
       dpi=300 )
Saving 7 x 7 in image

gnmds with density 1036 threshold

summary(env_sub_meta$swDensRob_avs)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1035    1036    1037    1037    1038    1041 
env_sub_meta$densCat1036<-cut(env_sub_meta$swDensRob_avs, 
      breaks=c(1035,1036,1041))

p_dens <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by density threshold 1036") +
  geom_point(aes(colour = factor(densCat1036))) +
   scale_colour_manual(values=c25)+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_dens)

NA
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_densityCat1036.png"),
       device = "png",
       dpi=300 )
Saving 7 x 7 in image

gnmds with bathy categories

summary(env_sub_meta$bathy)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  -2717   -2145   -1954   -1955   -1652   -1504 
env_sub_meta$bathyCat<-cut(env_sub_meta$bathy, 
      breaks=c(-2717,-2600,-2500,-2400,-2300,-2200,-2100,-2000,-1900,-1800,-1700,-1600,-1504),
      labels=c("> 2600m","2500-2600m","2400-2500m","2300-2400m","2200-2300m","2100-2200m",
               "2000-2100m","1900-2000m","1800-1900m","1700-1800m",
               "1600-1700m","< 1600m"))

p_bath <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by sediment class") +
  geom_point(aes(colour = bathyCat)) +
  scale_fill_binned()+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_bath)

NA
NA
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_bahtyCat.png"),
       device = "png",
       dpi=300 )
Saving 7 x 7 in image

gnmds with taxa


p_bath <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by sediment class") +
  geom_point(aes(colour = as.factor(Umbellula_encrinus))) +
  scale_fill_binned(type = "viridis")+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_bath)

NA
NA
Save the plot
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_bahtyCat.png"),
       device = "png",
       dpi=300 )

Save environment

EDIT AREA FIRST!

save.image("I:/Scripts/deepseaNiN/Renv_deepseaNiN_HiDensBelow1500m.RData") # edit area first

NOW CLEAR ENVIRONMENT BEFORE RUNNING FOR NEW AREA

---
title: "MAREANO - NiN:splitting biogeographic regions_HiDensBelow1500m"
output: html_notebook
---

Asssumes you have aready run:
- config
- workflow_deepseaNiN_Start

You now have the objects needed to explore the dataset and find the best way to apply biogeographic splits. This is a pre-step before running the workflow again on the split dataset to explore possible LKMs.

# Finding Regional (biogeographic) Splits

This requires exploring the dataset to theorise the best ways to split the data with:
- environmental biplots/colourings


then applying the splits. 

### Libraries
```{r}
library(plotly)
library(readxl)
```




## Exploring biplots
#### make full dataset to explore 
should include all env Vars and otus to explore, and make species richness variable

```{r}
#add species data and sp richness variable
env_sub_meta1<-cbind(env,otu_6)
env_sub_meta1$spRich<-rowSums(otu_6[,-c(1:which(colnames(otu_6)=="Zoanthidae"))]!=0)

#rename X
env_sub_meta1$X <- env$X.y
env_sub_meta1 <-env_sub_meta1 %>% select (-c(X.y))

#add samplID
env_sub_meta1$SampID<-envSel$SampID

```


### Add provisional biotope data to env file
Note that biotopes were last assigned in march 2022 and therefore there are some addional samples that have not yet got a biotope assigned. These should just be NAs
```{r}
biotopeInfo<-read_xlsx(file.path(dataPath, "inputs/MAREANO_provisional_biotope_classification_0322.xlsx"), sheet=1) %>%
  select(-c(x_coordinate_UTM33N, y_coordinate_UTM33N))

env_sub_meta<-left_join(env_sub_meta1,biotopeInfo)
```





### a colour palette for discrete plots (see later in script)

```{r}
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
```

### Find min max of vars for scales
```{r}
colnames(env_sub_meta)
```



### Plots with CONTINUOUS colour scales (variables)


##### Bathy v Temp w gnmds ax 1
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by gnmds r6 ax1")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_nmds1.png"),
       device = "png",
       dpi=300 )
```
##### Bathy v Density w gnmds ax 1
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = swDensRob_avs,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Density vs Bathy - coloured by gnmds1")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_bathy_v_dens_gnmds1.png"),
       device = "png",
       dpi=300 )
```





##### Bathy v Temp w longitude
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = X.y),
             size = 1) +
  scale_colour_gradientn(limits = c(-107939, 1162261),
                        colors=c('red','yellow','blue'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by longitude")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_longitude.png"),
       device = "png",
       dpi=300 )
```
##### Bathy v Temp w latitude
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = Y),
             size = 1) +
  scale_colour_gradientn(limits = c(6944134, 8949734),
                        colors=c('red','yellow','blue'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by latitude")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_latitude.png"),
       device = "png",
       dpi=300 )
```
##### Bathy v Temp w iceCovLTMax
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = BO22_icecoverltmax_ss),
             size = 1) +
  scale_colour_gradientn(limits = c(0,0.93),
                        colors=c('grey','turquoise','blue'))+
  ggtitle("Temp (mean Robinson) vs Bathy - coloured by ice cover LT max")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_temp_iceCovLTmax.png"),
       device = "png",
       dpi=300 )
```


##### Bathy v Salinity coloured by temp
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = bathy)) +
  theme_classic() +
  geom_point(aes(colour = Tmean_Robinson),
             size = 1) +
  scale_colour_gradientn(limits = c(-1.1, 8.5),
                        colors=c('red','yellow','green'))+
  ggtitle("Salinity (max Robinson) vs Bathy - coloured by av Temp (R)")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mbathy_v_salMax_temp.png"),
       device = "png",
       dpi=300 )
```

##### Salinity v Temp gnmds1
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = Tmean_Robinson)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) vs Salinity (max R) - coloured by gnmds r6 ax 1 - grey 2.5-5")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mtempRmean_v_salinityRmax_nmds1.png"),
       device = "png",
       dpi=300 )
```
##### Salinity v Temp dissoxmean
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = Tmean_Robinson)) +
  theme_classic() +
  geom_point(aes(colour = BO22_dissoxmean_bdmean),
             size = 1) +
scale_colour_gradientn(limits = c(282.5, 372.2),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) vs Salinity (max R) - coloured by dissolved oxygen")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mtempRmean_v_salinityRmax_disooxmean.png"),
       device = "png",
       dpi=300 )
```
##### TS - icecoverLTMax
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Smax_Robinson,
                          y = Tmean_Robinson)) +
  theme_classic() +
  geom_point(aes(colour = BO22_icecoverltmax_ss),
             size = 1) +
  scale_colour_gradientn(limits = c(0,0.93),
                        colors=c('blue','green','red'))+
  ggtitle("Temp (AvR) v Salinity (maxR) - coloured by ice cover LT max")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mtempRmean_v_salinityRmax_iceCoveLTmax.png"),
       device = "png",
       dpi=800 )
```


##### X v Y - gnmds1
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$gnmds1),
                                    max(env_sub_meta$gnmds1)),
                        colors=c('red','yellow','green'))+
  ggtitle("Geography (X v Y) - coloured by gnmds1")

ggplotly(tb_ax1)

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_gnmds1.png"),
       device = "png",
       dpi=300 )
```

##### X v Y - density
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = swDensRob_avs),
             size = 1) +
  scale_colour_gradientn(limits = c(min(env_sub_meta$swDensRob_avs), 
                                    max(env_sub_meta$swDensRob_avs)),
                        colors=c('blue','green','red'))+
  ggtitle("Geography (X v Y) - coloured by water density")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_swDenRobavs.png"),
       device = "png",
       dpi=800 )
```
##### X v Y - dissox
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = BO22_dissoxmean_bdmean),
             size = 1) +
  scale_colour_gradientn(limits = c(282,373),
                        colors=c('blue','green','red'))+
  ggtitle("Geography (X v Y) - coloured by dissovled oxygen")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_dissox.png"),
       device = "png",
       dpi=800 )
```
##### X v Y - icecoverLTMax
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = BO22_icecoverltmax_ss),
             size = 1) +
  scale_colour_gradientn(limits = c(0.00001,0.93),
                        colors=c('blue','green','red'))+
  ggtitle("Geography (X v Y) - coloured by ice cover LT max - grey <0.00001")

tb_ax1

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_X_v_Y_iceCoveLTmax.png"),
       device = "png",
       dpi=800 )
```


##### Temp v disoxltmin gnmds
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = BO22_dissoxmean_bdmean)) +
  theme_classic() +
  geom_point(aes(colour = gnmds1),
             size = 1) +
  scale_colour_gradientn(limits = c(-2.3, 2),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) v disox mean - coloured by gnmds ax 1")

tb_ax1

```

##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_TmeanR_v_dissoxltmin_gnmds1.png"),
       device = "png",
       dpi=300 )

```
##### Temp v disoxltmin bathy
```{r}
tb_ax1<- ggplot(data = env_sub_meta,
                      aes(x = Tmean_Robinson,
                          y = BO22_dissoxmean_bdmean)) +
  theme_classic() +
  geom_point(aes(colour = bathy),
             size = 1) +
  scale_colour_gradientn(limits = c(-702, -38),
                        colors=c('red','yellow','green'))+
  ggtitle("Temp (mean Robinson) v disox mean - coloured by bathymetry")

tb_ax1

```

##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500mTmeanR_v_dissoxltmin_bathy.png"),
       device = "png",
       dpi=300 )

```



### Plots with DISCRETE colour scales (variables)

#### X v Y with mld-bathy categories
```{r}
dis_split <- ggplot(data = env,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = MLDmean_bathy),
             size = 1) +
  scale_colour_manual(values=cbPalette)+
 # scale_colour_brewer(palette = "Set3") +
  ggtitle("Easting vs Northing - coloured by Mixed layer depth proximity")

dis_split

```

##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_MLDmeanBathy.png"),
       device = "png",
       dpi=300 )

```

### categorise gnmds

```{r}
env_sub_meta$ax1cat<-cut(env_sub_meta$gnmds1, 
      breaks=c(-3.2,-3,-2,-1,0,1,2,3,3.46))

env_sub_meta$ax2cat<-cut(env_sub_meta$gnmds2, 
      breaks=c(-1.9,-1,0,1,2,3,4,4.9))
```


#### X v Y with gnmds ax 1 as HC categories
```{r}
dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = ax1cat),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Spectral") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by gnmds axis 1 HC units")

dis_split

```

##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_ax1cat.png"),
       device = "png",
       dpi=300 )

```

### categorise temp 5.1

```{r}
env_sub_meta$temp5_1<-cut(env_sub_meta$Tmean_Robinson, 
      breaks=c(-1.1, 5.1, 8.5))
#
```


#### X v Y with temp 5.1
```{r}
dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = temp5_1),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by temp thresholded at 5.1*C")

dis_split

```

##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_temp5_1.png"),
       device = "png",
       dpi=300 )

```
### categorise dissox

```{r}
env_sub_meta$dissoxav305<-cut(env_sub_meta$BO22_dissoxmean_bdmean, 
     # breaks=c(256, 282, 360)) #ltmin
     breaks=c(282.5,305,372.2),
     labels=c("lowO2","hiO2")) #mean
#
```


#### X v Y with dissox
```{r}
dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = dissoxav305),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by dissox av thresholded at 305")

dis_split

```

##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_dissoxav305.png"),
       device = "png",
       dpi=300 )

```

### categorise iceCovLTMax

```{r}
env_sub_meta$iceMx_gt0<-cut(env_sub_meta$BO22_icecoverltmax_ss, 
      breaks=c(0, 0.00001, 0.92838))
#
```


#### X v Y with temp 5.1
```{r}
dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = iceMx_gt0),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by ice LT Max thresholded at >0")

dis_split

```

##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_iceLTmx_gt0.png"),
       device = "png",
       dpi=300 )

```




#### X v Y with gnmds ax 2 as HC categories
```{r}
dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = ax2cat),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Spectral") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by gnmds axis 2 HC units")

dis_split

```




##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_ax2cat.png"),
       device = "png",
       dpi=300 )

```

#### Gnmds w temp 5.1
```{r}

t_gmo <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by temp 5.1 threshold",
          subtitle = "First run") +
  geom_point(aes(colour = factor(temp5_5))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

t_gmo
```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_temp5_1.png"),
       device = "png",
       dpi=300 )
```
#### Gnmds w dissox 305
```{r}

o_gmo <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by dissox 305 threshold",
          subtitle = "First run") +
  geom_point(aes(colour = factor(dissox305))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

o_gmo
```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_dissox305.png"),
       device = "png",
       dpi=300 )
```

#### Gnmds w iceLT max >0
```{r}

i_gmo <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by ice max >0 threshold",
          subtitle = "First run") +
  geom_point(aes(colour = factor(iceMx_gt0))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

i_gmo
```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_iceMxgt0.png"),
       device = "png",
       dpi=300 )
```

```{r}
comp<-i_gmo+o_gmo+t_gmo
```
##### Save the plot
```{r}


##### Save some outputs

ggexport(comp,
          filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_compare.png"),
          width = 1500,
          height = 500)

```




#### X v Y with landscape
```{r}
dis_split <- ggplot(data = env_sub_meta,
              aes(x = X.y,
                  y = Y)) +
  theme_classic() +
  geom_point(aes(colour = as.factor(landscape)),
             size = 1) +
 # scale_colour_manual(values=cbPalette) +# non-ordered colourblind pallette
  scale_colour_brewer(palette = "Set1") + # ordered colourblind pallette
  ggtitle("Easting vs Northing - coloured by landscape")

dis_split

```

##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_XvY_landscape.png"),
       device = "png",
       dpi=300 )

```


# GNMDS PLOTS

###### colour palette to cope with up to 25 categorical colours
```{r}
c25 <- c(
  "dodgerblue2", "#E31A1C", # red
  "green4",
  "#6A3D9A", # purple
  "#FF7F00", # orange
  "black", "gold1",
  "skyblue2", "#FB9A99", # lt pink
  "palegreen2",
  "#CAB2D6", # lt purple
  "#FDBF6F", # lt orange
  "gray70", "khaki2",
  "maroon", "orchid1", "hiDens_b1500pink1", "blue1", "steelblue4",
  "darkturquoise", "green1", "yellow4", "yellow3",
  "darkorange4", "brown"
)
```



#### gnmds with density categories

```{r}
summary(env_sub_meta$swDensRob_avs)

env_sub_meta$densCat<-cut(env_sub_meta$swDensRob_avs, 
      breaks=c(1035,1036,1037,1038,1039,1040,1041))
```


```{r}

p_dens <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by density bins") +
  geom_point(aes(colour = factor(densCat))) +
   scale_colour_manual(values=c25)+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_dens)

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_densityCat.png"),
       device = "png",
       dpi=300 )

```


#### gnmds with density 1036 threshold

```{r}
summary(env_sub_meta$swDensRob_avs)

env_sub_meta$densCat1036<-cut(env_sub_meta$swDensRob_avs, 
      breaks=c(1035,1036,1041))
```


```{r}

p_dens <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by density threshold 1036") +
  geom_point(aes(colour = factor(densCat1036))) +
   scale_colour_manual(values=c25)+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_dens)

```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_densityCat1036.png"),
       device = "png",
       dpi=300 )

```





#### gnmds with bathy categories

```{r}
summary(env_sub_meta$bathy)

env_sub_meta$bathyCat<-cut(env_sub_meta$bathy, 
      breaks=c(-2717,-2600,-2500,-2400,-2300,-2200,-2100,-2000,-1900,-1800,-1700,-1600,-1504),
      labels=c("> 2600m","2500-2600m","2400-2500m","2300-2400m","2200-2300m","2100-2200m",
               "2000-2100m","1900-2000m","1800-1900m","1700-1800m",
               "1600-1700m","< 1600m"))
```


```{r}

p_bath <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by sediment class") +
  geom_point(aes(colour = bathyCat)) +
  scale_fill_binned()+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_bath)


```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_bahtyCat.png"),
       device = "png",
       dpi=300 )

```

# gnmds with taxa


```{r}

p_bath <- ggplot(data = env_sub_meta,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by sediment class") +
  geom_point(aes(colour = as.factor(Umbellula_encrinus))) +
  scale_fill_binned(type = "viridis")+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

ggplotly(p_bath)


```
##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/HiDensBelow1500m_gnmds_bahtyCat.png"),
       device = "png",
       dpi=300 )

```





# Save environment

EDIT AREA FIRST!

```{r}
save.image("I:/Scripts/deepseaNiN/Renv_deepseaNiN_HiDensBelow1500m.RData") # edit area first

```

NOW CLEAR ENVIRONMENT BEFORE RUNNING FOR NEW AREA

